Database Marketing

Base Knowledge

There are no prerequisites to attend this Course.

 

Teaching Methodologies

Classes will be theoretical and practical, with content exposition and application of concepts based on a Case Study to be worked on by groups of students – the objective is to allow students to build:
– notion of Database as a way of structuring identified data necessary to be collected;
– useful idea between identifying data, handling it and analyzing it to support Marketing functions, such as creating campaigns.
Based on an Excel dataset, they will use a Data Analytics tool (Power BI).

Learning Results

Objectives

  • OB1 – Understand the importance of data in marketing.
  • OB2 – Be able to describe the main concepts related to Database Marketing.
  • OB3 – Learn how to define, collect, analyse, understand and manage customer data in order to communicate more effectively and personally with customers.
  • OB4 – Understand and analyze data, with knowledge of the GDPR.
  • OB5 – Describe and apply data analysis techniques for Database Marketing, namely, amongst others, being able to segment customers.
  • OB6 – Know how to design, plan and test Database Marketing campaigns.
  • OB7 – Establish appropriate measures of a Database Marketing campaign.

 

Competences

  • CO1 – knowledge acquisition database management base
  • CO2 – use of digital tools in data analysis
  • CO3 – application of marketing principles in data analysis
  • CO4 – application of the concept of consumer behavior in the use of digital tools

Program

1. Database Marketing Fundamentals
1.1 History of Database Marketing
1.2 Understanding Database Marketing
2. Strategic development of databases for Marketing
2.1 Introduction
2.2 Definition of Internet Marketing
2.3 Internet Marketing Applications
3. Defining customer data requirements
3.1 Concepts
3.2 Data organization
4. Analysis of customer data
4.1 Data identification
4.2 Data modeling
5. Construction of databases
5.1 Concepts
5.2 Modeling Information Objects in a diagram
6. Data Mining and Customer Segmentation
6.1 Definition and techniques
6.2 Applications for customer segmentation
7. Plan and design Marketing tests and Marketing campaigns
8. Marketing Databases and the Internet
8.1 Databases for MK
8.2 Impact of the Internet

Curricular Unit Teachers

Maria Manuela de Sousa Freire

Grading Methods

Continuous assessment consists of:
• Final Written Assessment (AFE) - 50% - to be carried out on the day defined for the Normal Exam (date defined in accordance with the Lesson Plan).
• Practical Project (PP) - 50% - group presentation/defense on the last day of classes and subsequent delivery of the Report in InfoEstudante

Assessment by Exam consists of:
• Written Assessment in Exam (AEE) - 50%
• Practical Project (PP) - 50% - individual presentation/defense on the day defined for the Normal Exam (date defined according to the Class Plan) after AEE and subsequent delivery of the Report to InfoEstudante on a date to be defined by the teacher
Assessment by Exam has this composition whether it is a Normal Exam, an Appeal Exam or a Special Exam.

Each of the two components will be evaluated for 20 values. Each component has a minimum score of 8 (eight) values.

The Final Grade of the Curricular Unit will be calculated as the arithmetic mean of the two components, rounded to the nearest integer. Its formula is as follows:
Final Grade = (AFE+PP)/2 or (AEE+PP)/2, rounded to the nearest integer.

The PP component can be carried out in groups of 3 and 4 students. Exceptions are evaluated and approved by the teacher when consistent with the situation presented by email.

The grade obtained in the normal season for the PP component will be valid for the remaining seasons. This situation does not, however, invalidate the possibility of the student trying to improve that same grade.
If the PP component has not been delivered for evaluation in attendance, the student must submit the powerpoint (in PDF format) for evaluation up to 1 week before the date scheduled for the exam for the normal, recourse and/or special season, the powerpoint (in PDF format) on InfoEstudante which will be used to present/defend the work on the day of the written test.


    Internship(s)

    NAO

    Bibliography

    Fundamental

    • Burns, A. C., & Veeck, A. (2020). Marketing research. Pearson.
    • Chaffey, D., Ellis-Chadwick, F, Mayer, R., Johnston, K. (2006), Internet Marketing: Strategy, Implementation and Practice, Financial Times/ Prentice Hall; 3th edition.
    • De Bock, K.W., Neslin, S.A. (2013), Advanced Database Marketing Hardcover, Kristof Coussement, Gower.
    • Drozdenko, R.G., Drake, P.D. (2002), Optimal Database Marketing: Strategy, Development, and Data Mining, SAGE Publications.
    • Farris, P.W., Bendle, N.T., Pfeifer, P.E., Reibstein, D.J. (2010), Marketing Metrics: The Definitive Guide to Measuring Marketing Performance, Pearson, 2 edition.
    • Keegan, W.J., Green, M.C. (2012), Global Marketing Paperback, Prentice Hall; 7 edition.
    • Kingsnorth, S. (2022). Digital marketing strategy: an integrated approach to online marketing. Kogan Page Publishers.
    • Tapp, A., Whitten, I., Housden, M. (2013), Principles of Direct, Database and Digital Marketing, Pearson, 5 edition.

    Complementar
    • Blattberg, R. C., Kim, B., Neslin, S. A. (2009), Database Marketing: Analyzing and Managing Customers, Springer.
    • Campbell, C., & Ying, Y. (2022). Learning with support vector machines. Springer Nature.
    • Chaffey, D., & Smith, P. R. (2022). Digital marketing excellence: planning, optimizing and integrating online marketing. Taylor & Francis.
    • Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90-98.
    • Hughes, A.M. (2012), Strategic Database Marketing 4e: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program, McGraw-Hill Professional; 4 edition.
    • Kent, R. (2020). Data construction and data analysis for survey research. Bloomsbury Publishing.
    • Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. John Wiley & Sons.
    • Ratner, B. (2003), Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, Chapman & Hall/CRC.